“Popularity Effect” in User-Generated Content: Evidence from Online Product Reviews

Paulo B. Goes (), Mingfeng Lin () and Ching-man Au Yeung ()
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Paulo B. Goes: Eller College of Management, University of Arizona, Tucson, Arizona 85721
Mingfeng Lin: Eller College of Management, University of Arizona, Tucson, Arizona 85721
Ching-man Au Yeung: Axon Labs Ltd., Hong Kong

Information Systems Research, 2014, vol. 25, issue 2, 222-238

Abstract: Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews? More importantly, what kind of reviews do such interactions induce? We study these questions using data from one of the largest product review websites where users can subscribe to one another. By applying both panel data and a flexible matching method, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings also systematically change and become more negative and more varied. Such trade-off has not been previously documented and has important implications for both product review and other user-generated content websites.

Keywords: product reviews; user-generated content; online community; opinion leader; social media; popularity; text mining; matching (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:25:y:2014:i:2:p:222-238

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